


import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 							lowerCamelCase__						(				UpperCAmelCase       ,						UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		IFInpaintingSuperResolutionPipeline
			_UpperCamelCase		:       Union[str, Any] 			=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
			_UpperCamelCase		:       List[str] 			=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}   )
			_UpperCamelCase		:       Optional[Any] 			=		PipelineTesterMixin.required_optional_params - {'latents'}




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return self._get_superresolution_dummy_components()




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case=0  ):

						'''simple docstring'''


						if str(snake_case  ).startswith("mps"  ):
									UpperCamelCase__       =							torch.manual_seed(snake_case  )
						else:
									UpperCamelCase__       =							torch.Generator(device=snake_case  ).manual_seed(snake_case  )

						UpperCamelCase__       =							floats_tensor((1, 3, 16, 16)   ,    rng=random.Random(snake_case  )  ).to(snake_case  )
						UpperCamelCase__       =							floats_tensor((1, 3, 32, 32)   ,    rng=random.Random(snake_case  )  ).to(snake_case  )
						UpperCamelCase__       =							floats_tensor((1, 3, 32, 32)   ,    rng=random.Random(snake_case  )  ).to(snake_case  )

						UpperCamelCase__       =							{
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": image,
						    "original_image": original_image,
						    "mask_image": mask_image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "output_type": "numpy",
						}

						return inputs




			@unittest.skipIf(
			    torch_device != "cuda" or not is_xformers_available()   ,    reason="XFormers attention is only available with CUDA and `xformers` installed"   ,    )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_save_load_optional_components()




			@unittest.skipIf(torch_device != "cuda"   ,    reason="float16 requires CUDA"  )
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().test_save_load_floataa(expected_max_diff=1E-1  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_save_load_local()







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2   ,    )






from __future__ import annotations

import numpy as np
def 							UpperCamelCase_(					_A :list[float]	)->		Optional[Any]:
			return np.maximum(0					,  _A	)


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


